Comprehensive evaluation of electrophysiological and 3D structural features of human atrial myocardium with insights on atrial fibrillation maintenance mechanisms

Mikhailov, Aleksei; Kalyanasundaram, Anuradha; Li, Ning; Scott, Shane S.; Artiga, Esthela J.; Subr, Megan; Zhao, Jichao; Hansen, Brian J.; Hummel, John D.; Fedorov, Vadim V.

doi:10.1016/j.yjmcc.2020.10.012

Cited by 14 publications

(13 citation statements)

References 231 publications

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“…Examples include [7, 8], identifying several key factors in the formation of the reentrant substrate. Of particular note are the orientation of muscle fibres, the structure, thickness, and thickness gradients of the atria, and the accumulation of fibrosis, particularly interstitial fibrosis [27].…”

Section: Discussionmentioning

confidence: 99%

Identifying locations susceptible to micro-anatomical reentry using a spatial network representation of atrial fibre maps

Falkenberg

Coleman

Dobson

et al. 2021

Preprint

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Micro-anatomical reentry has been identified as a potential driver of atrial fibrillation (AF). In this paper, we introduce a novel computational method which aims to identify which atrial regions are most susceptible to micro-reentry. The approach, which considers the structural basis for micro-reentry only, is based on the premise that the accumulation of electrically insulating interstitial fibrosis can be modelled by simulating percolation-like phenomena on spatial networks. Our results suggest that at high coupling, where micro-reentry is rare, the micro-reentrant substrate is highly clustered in areas where the atrial walls are thin and have convex wall morphology. However, as transverse connections between fibres are removed, mimicking the accumulation of interstitial fibrosis, the substrate becomes less spatially clustered, and the bias to forming in thin, convex regions of the atria is reduced. Comparing our algorithm on image-based models with and without atrial fibre structure, we find that strong longitudinal fibre coupling can suppress the micro-reentrant substrate, whereas regions with disordered fibre orientations have an enhanced risk of micro-reentry. We suggest that with further development, these methods may have future potential for patient-specific risk stratification, taking a longitudinal view of the development of the micro-reentrant substrate.

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Section: Discussionmentioning

confidence: 99%

Identifying locations susceptible to micro-anatomical reentry using a spatial network representation of atrial fibre maps

Falkenberg

Coleman

Dobson

et al. 2021

Preprint

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“…The regional differences in action potential morphology have typically been incorporated by changing the maximum conductance and gating variables of ion channel models, and differences in conduction velocity can be represented in models by spatially changing the conductivity of the tissue according to the tensor vector obtained from the fiber direction information [117]. The 3D virtual human atria not only allows to differentiate the relative contribution of each variable (i.e., gene variants, ion channels, calcium handling proteins or structural features) to AF mechanisms, but also allows for the development of personalized treatments (e.g., targeted ablation planning and antiarrhythmic drug selection) [10] (Figure 9). calcium handling proteins or structural features) to AF mechanisms, but also allows for the development of personalized treatments (e.g., targeted ablation planning and antiarrhythmic drug selection) [10] (Figure 9).…”

Section: Geometric and Image-based Atrial Modelingmentioning

confidence: 99%

“…The 3D virtual human atria not only allows to differentiate the relative contribution of each variable (i.e., gene variants, ion channels, calcium handling proteins or structural features) to AF mechanisms, but also allows for the development of personalized treatments (e.g., targeted ablation planning and antiarrhythmic drug selection) [10] (Figure 9). calcium handling proteins or structural features) to AF mechanisms, but also allows for the development of personalized treatments (e.g., targeted ablation planning and antiarrhythmic drug selection) [10] (Figure 9). Figure 9.…”

Section: Geometric and Image-based Atrial Modelingmentioning

confidence: 99%

“…In the third stage, the development of actionable personalized approaches, which take into account patient-specific profiles and arrhythmia mechanisms, will likely be essential to overcome current challenges in AF management ( Figure 1 B). Computational modelling and simulation are indispensable in cardiac electrophysiology in the study of complex arrhythmias [ 10 , 11 , 12 ], such as AF. The multi-scale model of cardiac electrophysiology provides a framework that can integrate experimental and clinical findings [ 13 ], and link micro-scale phenomena to emerging behaviors of the entire organ [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

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Understanding PITX2-Dependent Atrial Fibrillation Mechanisms through Computational Models

Bai

Zhu

et al. 2021

IJMS

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Atrial fibrillation (AF) is a common arrhythmia. Better prevention and treatment of AF are needed to reduce AF-associated morbidity and mortality. Several major mechanisms cause AF in patients, including genetic predispositions to AF development. Genome-wide association studies have identified a number of genetic variants in association with AF populations, with the strongest hits clustering on chromosome 4q25, close to the gene for the homeobox transcription PITX2. Because of the inherent complexity of the human heart, experimental and basic research is insufficient for understanding the functional impacts of PITX2 variants on AF. Linking PITX2 properties to ion channels, cells, tissues, atriums and the whole heart, computational models provide a supplementary tool for achieving a quantitative understanding of the functional role of PITX2 in remodelling atrial structure and function to predispose to AF. It is hoped that computational approaches incorporating all we know about PITX2-related structural and electrical remodelling would provide better understanding into its proarrhythmic effects leading to development of improved anti-AF therapies. In the present review, we discuss advances in atrial modelling and focus on the mechanistic links between PITX2 and AF. Challenges in applying models for improving patient health are described, as well as a summary of future perspectives.

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“…The regional differences in action potential morphology have typically been incorporated by changing the maximum conductance and gating variables of ion channel models, and differences in conduction velocity can be represented in models by spatially changing the conductivity of the tissue according to the tensor vector obtained from the fiber direction information [132]. The 3D virtual human atria not only allows to differentiate the relative contribution of each variable (i.e., gene variants, ion channels, calcium handling proteins or structural features) to AF mechanisms, but also allows for the development of personalized treatments (e.g., targeted ablation planning and antiarrhythmic drug selection) [40] (Fig. 9).…”

Section: Geometric and Image-based Atrial Modelingmentioning

confidence: 99%

Understanding PITX2-dependent Atrial Fibrillation Mechanisms through Computational Models

Bai¹,

Lu²,

Zhu³

et al. 2021

Preprint

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Atrial fibrillation (AF) is a common arrhythmia. Better prevention and treatment of AF are needed to reduce AF-associated morbidity and mortality. Several major mechanisms cause AF in patients, including a genetic predisposition to develop AF. Genome-wide association studies have identified genetic variants associated with AF populations, with the strongest hits clustering on chromosome 4q25, close to the gene coding for the homeobox transcription PITX2. Because of the inherent complexity of the human heart, experimental and basic research on PITX2-dependent AF is not sufficient for understanding atrial functional proprieties. Linking PITX2 to ion channels, cells, tissues, atria and the whole heart, computational models are necessary for achieving a quantitative understanding of atrial structure and function in PITX2-dependent AF. Computational approaches are used to capture all that we know about PITX2-dependent AF and to develop improved therapies. In the present review, we discuss advances in atrial modelling and focus on the mechanistic links between PITX2 and AF. Challenges in applying models for improving patient health are described, as well as a summary of future perspectives.

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Comprehensive evaluation of electrophysiological and 3D structural features of human atrial myocardium with insights on atrial fibrillation maintenance mechanisms

Cited by 14 publications

References 231 publications

Identifying locations susceptible to micro-anatomical reentry using a spatial network representation of atrial fibre maps

Identifying locations susceptible to micro-anatomical reentry using a spatial network representation of atrial fibre maps

Understanding PITX2-Dependent Atrial Fibrillation Mechanisms through Computational Models

Understanding PITX2-dependent Atrial Fibrillation Mechanisms through Computational Models

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